Learning Hard Alignments with Variational Inference

There has recently been significant interest in hard attention models for tasks such as object recognition, visual captioning and speech recognition. Hard attention can offer benefits over soft attention such as decreased computational cost, but training hard attention models can be difficult because of the discrete latent variables they introduce. Previous work used REINFORCE and Q-learning to approach these issues, but those methods can provide high-variance gradient estimates and be slow to train. In this paper, we tackle the problem of learning hard attention for a sequential task using variational inference methods, specifically the recently introduced VIMCO and NVIL. Furthermore, we propose a novel baseline that adapts VIMCO to this setting. We demonstrate our method on a phoneme recognition task in clean and noisy environments and show that our method outperforms REINFORCE, with the difference being greater for a more complicated task

Online Change Points Detection for Linear Dynamical Systems with Finite Sample Guarantees

The problem of online change point detection is to detect abrupt changes in properties of time series, ideally as soon as possible after those changes occur. Existing work on online change point detection either assumes i.i.d data, focuses on asymptotic analysis, does not present theoretical guarantees on the trade-off between detection accuracy and detection delay, or is only suitable for detecting single change points. In this work, we study the online change point detection problem for linear dynamical systems with unknown dynamics, where the data exhibits temporal correlations and the system could have multiple change points. We develop a data-dependent threshold that can be used in our test that allows one to achieve a pre-specified upper bound on the probability of making a false alarm. We further provide a finite-sample-based bound for the probability of detecting a change point. Our bound demonstrates how parameters used in our algorithm affect the detection probability and delay, and provides guidance on the minimum required time between changes to guarantee detection.Comment: 11 pages, 3 figure

Learning Linearized Models from Nonlinear Systems with Finite Data

Identifying a linear system model from data has wide applications in control theory. The existing work on finite sample analysis for linear system identification typically uses data from a single system trajectory under i.i.d random inputs, and assumes that the underlying dynamics is truly linear. In contrast, we consider the problem of identifying a linearized model when the true underlying dynamics is nonlinear. We provide a multiple trajectories-based deterministic data acquisition algorithm followed by a regularized least squares algorithm, and provide a finite sample error bound on the learned linearized dynamics. Our error bound demonstrates a trade-off between the error due to nonlinearity and the error due to noise, and shows that one can learn the linearized dynamics with arbitrarily small error given sufficiently many samples. We validate our results through experiments, where we also show the potential insufficiency of linear system identification using a single trajectory with i.i.d random inputs, when nonlinearity does exist.Comment: 8 pages, 3 figures, IEEE Conference on Decision and Control, 202

Fast Arithmetics Using Chinese Remaindering

In this paper, some issues concerning the Chinese remaindering representation are discussed. Some new converting methods, including an efficient probabilistic algorithm based on a recent result of von zur Gathen and Shparlinski \cite{Gathen-Shparlinski}, are described. An efficient refinement of the NC$^1$ division algorithm of Chiu, Davida and Litow \cite{Chiu-Davida-Litow} is given, where the number of moduli is reduced by a factor of $\log n$

Flight Mechanics and Control of Escape Manoeuvres in Hummingbirds. I. Flight Kinematics

Hummingbirds are nature’s masters of aerobatic manoeuvres. Previous research shows that hummingbirds and insects converged evolutionarily upon similar aerodynamic mechanisms and kinematics in hovering. Herein, we use three-dimensional kinematic data to begin to test for similar convergence of kinematics used for escape flight and to explore the effects of body size upon manoeuvring. We studied four hummingbird species in North America including two large species (magnificent hummingbird, Eugenes fulgens, 7.8 g, and blue-throated hummingbird, Lampornis clemenciae, 8.0 g) and two smaller species (broad-billed hummingbird, Cynanthus latirostris, 3.4 g, and black-chinned hummingbirds Archilochus alexandri, 3.1 g). Starting from a steady hover, hummingbirds consistently manoeuvred away from perceived threats using a drastic escape response that featured body pitch and roll rotations coupled with a large linear acceleration. Hummingbirds changed their flapping frequency and wing trajectory in all three degrees of freedom on a stroke-by-stroke basis, likely causing rapid and significant alteration of the magnitude and direction of aerodynamic forces. Thus it appears that the flight control of hummingbirds does not obey the ‘helicopter model’ that is valid for similar escape manoeuvres in fruit flies. Except for broad-billed hummingbirds, the hummingbirds had faster reaction times than those reported for visual feedback control in insects. The two larger hummingbird species performed pitch rotations and global-yaw turns with considerably larger magnitude than the smaller species, but roll rates and cumulative roll angles were similar among the four species

Flight Mechanics and Control of Escape Manoeuvres in Hummingbirds. II. Aerodynamic Force Production, Flight Control and Performance Limitations

The superior manoeuvrability of hummingbirds emerges from complex interactions of specialized neural and physiological processes with the unique flight dynamics of flapping wings. Escape manoeuvring is an ecologically relevant, natural behaviour of hummingbirds, from which we can gain understanding into the functional limits of vertebrate locomotor capacity. Here, we extend our kinematic analysis of escape manoeuvres from a companion paper to assess two potential limiting factors of the manoeuvring performance of hummingbirds: (1) muscle mechanical power output and (2) delays in the neural sensing and control system. We focused on the magnificent hummingbird (Eugenes fulgens, 7.8 g) and the black-chinned hummingbird (Archilochus alexandri, 3.1 g), which represent large and small species, respectively. We first estimated the aerodynamic forces, moments and the mechanical power of escape manoeuvres using measured wing kinematics. Comparing active-manoeuvring and passive-damping aerodynamic moments, we found that pitch dynamics were lightly damped and dominated by the effect of inertia, while roll dynamics were highly damped. To achieve observed closed-loop performance, pitch manoeuvres required faster sensorimotor transduction, as hummingbirds can only tolerate half the delay allowed in roll manoeuvres. Accordingly, our results suggested that pitch control may require a more sophisticated control strategy, such as those based on prediction. For the magnificent hummingbird, we estimated that escape manoeuvres required muscle mass-specific power 4.5 times that during hovering. Therefore, in addition to the limitation imposed by sensorimotor delays, muscle power could also limit the performance of escape manoeuvres

Development of Integration Software for Multiple Inkjet Functionalization Systems

Inkjet printing is widely used in functional product manufacturing. Performing a printing task requires communication and synchronization among multiple subsystems (e.g. motion and drop ejection), which introduces complexity in the overall printing system. A user interface has been developed, which enables users to input printing parameters and patterns for printing functional materials. The interface then sends commands to the controllers that execute the printing process. The software can also be expanded to carry out standard experiments for functional printing research and characterization. Moreover, the software is transferable to multiple systems. One application explored using the software is drug anti- counterfeiting research by printing edible coloring onto pills

An Incremental and Optimized Learning Method for the Automatic Classification of Protein Crystal Images

Protein production has experienced great advances in recent years. In particular, high throughput protein production, coupled with the use of robotics, outputs thousands of mixtures in micro-array wells. To detect the presence of protein crystal formation, images of these wells are acquired regularly using robotic cameras. Traditionally, a crystallographer would manually process each image — identifying the wells that resulted in protein crystal formation. This manual inspection process is slow and given the high rate of mixture output, it has become near impossible for crystallographers keep up. Our aim is to create an automated method of detecting which wells have crystals and which ones do not. We make use of a neural network that is trained based on manually classified ground truth data. After it is trained, the automatic classifier would give a binary output — a value of one for the detection of crystals and precipitates in images and a value of zero otherwise. In our previous papers, the core methods of using multi-scale Laplacian image representation to extract image features and the implementation of the neural network classifier were discussed. Here we present a new, optimized approach to training the neural network and results from a large-scale test. We claim that the neural network can be better trained if the training image dataset is optimized in the sense that ambiguous images are removed during the initial training processes. Incremental training is implemented so that the network can be improved as more data becomes available. From initial results with training based on a 6,000 optimized image dataset, the accuracy of the improved classifier approaches 95% in identifying a wide array of images